The convergence properties of a recently developed recursive subspace identification algorithm are investigated in this paper. The algorithm operates on the basis of an extended instrumental variable (EIV) version of the propagator method for signal subspace estimation. It is proved that, under weak conditions on the input signal and the identified system, the considered MOESP class of recursiv...

The convergence properties of recently developed recursive subspace identification methods are investigated in this paper. The algorithms operate on the basis of instrumental variable (IV) versions of the propagator method for signal subspace estimation. It is proved that, under suitable conditions on the input signal and the system, the considered recursive subspace identification algorithms c...

The identification of multivariable state space models in innovation form is solved in a subspace identification framework using convex nuclear norm optimization. The convex optimization approach allows to include constraints on the unknown matrices in the data-equation characterizing subspace identification methods, such as the lower triangular block-Toeplitz of weighting matrices constructed ...

The aim of this contribution is to analyze a class of state-space subspace system identiication (4SID) methods. In particular, the eeect of diierent weighting matrices is studied. By a linear regression formulation, diierent cost-functions, which are rather implicit in the ordinary framework of 4SID, are compared. Expressions for asymptotic variances of pole estimation error are analyzed and fr...

This article introduces a tensor network subspace algorithm for the identification of specific polynomial state space models. The polynomial nonlinearity in the state space model is completely written in terms of a tensor network, thus avoiding the curse of dimensionality. We also prove how the block Hankel data matrices in the subspace method can be exactly represented by low rank tensor netwo...

A subspace identification algorithm for state-affine state-space systems which allows to approximate nonlinear systems arbitrarily well is derived. The proposed algorithm depends on an approximation step where a detailed approximation error analysis is provided. A special case is presented in which this approximation error vanishes. To tackle higher-order systems and ill-posed problems a regula...

We give a general overview of the state-of-the-art in subspace system identiication methods. We have restricted ourselves to the most important ideas and developments since the methods appeared in the late eighties. First, the basics of linear subspace identiication are summarized. Diierent algorithms one nds in literature (such as N4SID, IV-4SID, MOESP, CVA) are discussed and put into a unifyi...